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Identification of Hypertension Subgroups through Topological Analysis of Symptom-Based Patient Similarity

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Abstract

Objective

To obtain the subtypes of the clinical hypertension population based on symptoms and to explore the relationship between hypertension and comorbidities.

Methods

The data set was collected from the Chinese medicine (CM) electronic medical records of 33,458 hypertension inpatients in the Affiliated Hospital of Shandong University of Traditional Chinese Medicine between July 2014 and May 2017. Then, a hypertension disease comorbidity network (HDCN) was built to investigate the complicated associations between hypertension and their comorbidities. Moreover, a hypertension patient similarity network (HPSN) was constructed with patients’ shared symptoms, and 7 main hypertension patient subgroups were identified from HPSN with a community detection method to exhibit the characteristics of clinical phenotypes and molecular mechanisms. In addition, the significant symptoms, diseases, CM syndromes and pathways of each main patient subgroup were obtained by enrichment analysis.

Results

The significant symptoms and diseases of these patient subgroups were associated with different damaged target organs of hypertension. Additionally, the specific phenotypic features (symptoms, diseases, and CM syndromes) were consistent with specific molecular features (pathways) in the same patient subgroup.

Conclusion

The utility and comprehensiveness of disease classification based on community detection of patient networks using shared CM symptom phenotypes showed the importance of hypertension patient subgroups.

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Author information

Authors and Affiliations

  1. Clinical Research Base, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, China

    Yi-fei Wang & Wei Peng

  2. Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Jing-jing Wang, Zi-xin Shu & Xue-zhong Zhou

  3. Department of Urology, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, China

    Yong-hao Ren & Wei Li

  4. Information Center, the Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, China

    Chao Gao, Rui Wang & Xiao-feng Wang

  5. The First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China

    Yun-lun Li

  6. College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China

    Song-jun Han, Jia-ming Huan & Hai-yan Wang

  7. College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China

    Jia-yu Lyu

  8. Cardiovascular Department, Pingyin Hospital of Traditional Chinese Medicine, Jinan, 250400, China

    Cui Chen

Authors
  1. Yi-fei Wang
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  2. Jing-jing Wang
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  3. Wei Peng
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  4. Yong-hao Ren
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  5. Chao Gao
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  6. Yun-lun Li
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  7. Rui Wang
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  8. Xiao-feng Wang
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  9. Song-jun Han
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  10. Jia-yu Lyu
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  11. Jia-ming Huan
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  12. Cui Chen
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  13. Hai-yan Wang
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  14. Zi-xin Shu
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  15. Xue-zhong Zhou
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  16. Wei Li
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Contributions

Li W and Zhou XZ conceived and designed the experiments; Peng W, Li YL, Ren YH, Chen C, and Wang HY standardized the terms of symptoms and diagnoses; Wang YF and Wang JJ performed the experiments; Huan JM, Gao C, Wang R, Wang XF, Han SJ, Lyu JY, and Shu ZX analysed the data; Wang JJ wrote the manuscript; Zhou XZ and Li W revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Wei Li.

Ethics declarations

The authors declare that the research was conducted in the absence of any commercial and financial relationships that could be construed as a potential conflict of interest.

Additional information

Supported by the National Key Research and Development Project of China (No. 2017YFC1703502 and No. 2017YFC1703506)

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Wang, Yf., Wang, Jj., Peng, W. et al. Identification of Hypertension Subgroups through Topological Analysis of Symptom-Based Patient Similarity. Chin. J. Integr. Med. 27, 656–665 (2021). https://doi.org/10.1007/s11655-021-3336-3

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  • DOI: https://doi.org/10.1007/s11655-021-3336-3

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